9 papers
Motion-Guided Semantic Alignment with Negative Prompts for Zero-Shot Video Action Recognition
Yiming Wang, Frederick W. B. Li, Jingyun Wang
Zero-shot action recognition is challenging due to the semantic gap between seen and unseen classes. We present a novel framework that enhances CLIP with disentangled embeddings an…
Specificity-aware reinforcement learning for fine-grained open-world classification
Samuele Angheben, Davide Berasi, Alessandro Conti +2
Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and specific. Recent reasoning Large Mu…
Training-free Online Video Step Grounding
Luca Zanella, Massimiliano Mancini, Yiming Wang +2
Given a task and a set of steps composing it, Video Step Grounding (VSG) aims to detect which steps are performed in a video. Standard approaches for this task require a labeled tr…
ConViS-Bench: Estimating Video Similarity Through Semantic Concepts
Benedetta Liberatori, Alessandro Conti, Lorenzo Vaquero +3
What does it mean for two videos to be similar? Videos may appear similar when judged by the actions they depict, yet entirely different if evaluated based on the locations where t…
Can Text-to-Video Generation help Video-Language Alignment?
Luca Zanella, Massimiliano Mancini, Willi Menapace +3
Recent video-language alignment models are trained on sets of videos, each with an associated positive caption and a negative caption generated by large language models. A problem…
On Large Multimodal Models as Open-World Image Classifiers
Alessandro Conti, Massimiliano Mancini, Enrico Fini +3
Traditional image classification requires a predefined list of semantic categories. In contrast, Large Multimodal Models (LMMs) can sidestep this requirement by classifying images…